Young people frequently struggle with stress disorder as a result of changing cultures, lifestyles, and competition. Stress is one of the mental illnesses that can have varying degrees of negative effects on both physical and mental health, according to medical science. The number of those affected by this ailment is increasing daily, and the proportion of teens affected is skyrocketing. As they transition from adolescence to adulthood, teenagers face a variety of challenges, many of which are made worse by their exposure to social media technology. Therefore, it's crucial to comprehend the myriad factors that contribute to stress and to pinpoint the traits that are the main causes so that appropriate measures may be taken to effectively manage it. The evaluation of student stress at several different educational institutions begins with this study. In order to identify the most significant and significant elements, we will utilise machine learning techniques to evaluate stress patterns in this work. The information we are utilising for this was gathered through a survey given to graduate and postgraduate students at several campuses and institutions. After cleaning and preparing our data, a variety of machine learning algorithms have been employed to train and evaluate our data. Academic, economical, and social aspects were shown to be major characteristics that impact stress utilising Decision Tree and Support Vector Machine. Additionally, Neural network is recognised as the Best approach for the presented data. With the aid of these findings, one may recommend the ideal model for worry data in addition to perhaps aiding in the recommendation of stress-reduction techniques like yoga to the pupils.

Stress, Machine Learning, Random Forest, support vector machine